Distracting behaviors among teenagers and young, middle-aged, and older adult drivers when driving without and with warnings from an integrated vehicle safety system

Kidd, David G.; Buonarosa, Mary Lynn

doi:10.1016/j.jsr.2017.02.017

Cited by 15 publications

(18 citation statements)

References 18 publications

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“…The studies were thematically characterized in relation to the intervention type and delivery methods to help inform our model program in development. Table 2 indicates the five categories of interventions employed by design: presentations, videos, or brief instructional programs ( n = 5) (33–37); education or training programs ( n = 2) (38, 39); driving simulators ( n = 2) (40, 41); in-vehicle monitoring or feedback studies ( n = 3), (42–44); and integrated programs ( n = 5) (45–49).…”

Section: Resultsmentioning

confidence: 99%

“…These methods are easier to present in multiple locations and to larger groups. Alternatively, on-road assessments via self-evaluation or driving performance feedback (33, 42–44, 46, 47) and driving simulation interventions (40, 45–49) provided the ability to learn skills in a real-world setting or simulated environment representative of the real world. Interventions that were PC-based, web-based, or through an e-learning platform had the benefit of remote delivery to teens, parents and other users.…”

Section: Resultsmentioning

confidence: 99%

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An Integrative Review on Teen Distracted Driving for Model Program Development

Classen

Winter

Brown

et al. 2019

Front. Public Health

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Distracted driving, especially driver inattention, is associated with high levels of crash-related fatalities and injury. Teen novice drivers are one of the groups most likely to drive distracted and to suffer its consequences. Teens have a higher risk of engaging in texting or secondary tasks, e.g., eating while driving. Distracted driving interventions to date aim to improve teen and societal safety, but few have achieved effectiveness. A need exists for effective evidence-based distracted driving interventions. We used an integrative review to identify rigorous evidence, and inform the development of a teen distracted driving educational intervention. This five-step review included: identifying the research problem; collecting literature; evaluating literature; synthesizing data; and presenting results. We searched 6 databases, identifying 185 articles. Following three rounds of inclusion screening (title, abstract, and full-text), captured according to a PRISMA flow chart, 17 studies met inclusion. We categorized these studies, conducted in the U.S., as five intervention types that used approaches including presentations, videos or instructional programs, education or training programs, driving simulator training, in-vehicle monitoring or feedback, and integrated programs. Study designs included randomized controlled trials pre-post, quasi-experimental, and experimental designs with prospective longitudinal cohorts. The studies were heterogeneous in design, intervention and outcome. However, three core themes emerged across studies: i.e., hazard awareness, hazard mitigation and attention maintenance are primary critically necessary skills to prevent distracted driving; engaging a parent or adult as a partner in the intervention process from classroom to car contributed to the effectiveness of the intervention; and leveraging technology in training enhanced the effectiveness of the intervention. Study limitations pertained to a focus on short-term effects; sampling distributions that did not account for gender, age, race, and/or ethnicity; types of interventions; and bias. The limitations affect the generalizability of included study findings and, potentially, the review findings, as they may not apply to populations or contexts outside those synopsized. Strengths included our team's expertise in conducting evidence-based reviews, support of a health science librarian, and use of international review guidelines. As an outcome, we are applying findings of the integrated review to develop a computer-based training addressing teen distracted driving.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

An Integrative Review on Teen Distracted Driving for Model Program Development

Classen

Winter

Brown

et al. 2019

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Although many studies were able to detect the presence of HDS, most of them only used a single way of inducing a hazardous state – e.g., cell phone use ( Beede and Kass, 2006 ), high speeds ( Kidd and Buonarosa, 2017 ) or driving in dense traffic ( Fastenmeier and Gstalter, 2007 ). While a few studies do use multiple ways to induce hazardous states in drivers (e.g., Liang and Lee, 2010 ), they do not attempt to identify the cause of the hazardous state.…”

Section: Introductionmentioning

confidence: 99%

“…While a few studies do use multiple ways to induce hazardous states in drivers (e.g., Liang and Lee, 2010 ), they do not attempt to identify the cause of the hazardous state. Furthermore, many real-world and simulated driving studies that involve physiological measurements focus on a single session ( Healey and Picard, 2005 ; Kidd and Buonarosa, 2017 ). This may not capture within-subject variability (e.g., the same driver may have different reactions at different times of day), so studies should ideally involve multiple sessions (as done, e.g., with three drivers by Healey and Picard, 2005 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements

et al. 2018

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Drivers’ hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25–50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver’s hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver’s hazardous state, which could serve as the basis for more intelligent intervention systems.

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“…Although distracted driving is commonly related to novice drivers and cell phone use, the threat to traffic safety includes the entire population of operators and more secondary behaviors than cell phone distraction alone. Research studied the likelihood of teenagers, young, middle-aged, and older adult drivers to engage in secondary tasks while operating a vehicle (Kidd and Buonarosa, 2017). The participants drove an instrumented sedan that included video cameras focused on the driver.…”

Section: Driver Behaviormentioning

confidence: 99%

An analysis of the crash risk and likelihood of engaging in a distraction while driving using naturalistic, time-series data

Kirsch¹

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Distracting behaviors among teenagers and young, middle-aged, and older adult drivers when driving without and with warnings from an integrated vehicle safety system

Cited by 15 publications

References 18 publications

An Integrative Review on Teen Distracted Driving for Model Program Development

An Integrative Review on Teen Distracted Driving for Model Program Development

Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements

An analysis of the crash risk and likelihood of engaging in a distraction while driving using naturalistic, time-series data

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